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IEEE India Conference (INDICON)

作者： Pouya Bashivan Alireza Fatehi Ehsan Peymani Advanced Process Automation and Control (APAC) Research Group Department of Control Engineering K.N. Toosi University of Technology Tehran Iran

A multiple-model adaptive controller is developed using the self-organizing map (SOM) neural network. The considered controller which we name it as multiple controller via SOM (MCSOM) is evaluated on the pH neutralization plant. An improved switching algorithm based on excitation level of plant has also been suggested for systems with noisy environments. Identification of pH plant using SOM is discussed and performance of the multiple-model controller is compared to the self tuning regulator (STR) controller.

关键词： Neural networks Automatic control Adaptive control Clustering algorithms Switches Programmable control Mathematical model State feedback Performance analysis Estimation error

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Disturbance Rejection in Neural Network Model Predictive control

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IFAC Proceedings Volumes 2008年第2期41卷 3527-3532页

作者： Alireza Fatehi Houman Sadjadian Ali Khaki-Sedigh Ali Jazayeri Advanced Process Automation and Control (APAC) Research Group Faculty of Electrical Eng. K. N. Toosi University of Technology Tehran Iran

Neural Network Model Predictive control (NN-MPC) combines reliable prediction of neural network with excellent performance of model predictive control using nonlinear Levenberg-Marquardt optimization. It is shown that this structure is prone to steady-state error when external disturbances enter or actual system varies from its model. In this paper, these model uncertainties are taken into account using a disturbance model with iterative learning which adaptively change the learning rate to treat gradual effect of the model mismatch differently from the drastic changes of external disturbance. Then, a high-pass filter on error signal is designed to distinguish disturbances from model mismatches. Practical implementation results as well as simulation results demonstrate good performance of the proposed control method.

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An experimental comparison of adaptive controllers on a pH neutralization pilot plant

An experimental comparison of adaptive controllers on a pH n...

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IEEE India Conference (INDICON)

作者： Ehsan Peymani Alireza Fatehi Pouya Bashivan Ali Khaki Sedigh Advanced Process Automation & Control (APAC) Research Group K.N. Toosi University of Technology Tehran Iran

Inherent nonlinearity of pH processes causes that they are recognized as an appropriate test bench for evaluation of advanced controllers. Because of special characteristics of them, it is evident that adaptive controllers outperform others. This paper presents a comparison between a conventional adaptive controller and a switching multiple-model adaptive one in both regulation and disturbance rejection points of view. A disturbance rejection supervisor is designed to improve the performance of the adaptive controllers in the presence of unmeasured disturbances. A laboratory scale pH process is used as an application example.

关键词： Programmable control Adaptive control process control Automatic control Power system modeling Chemical processes Nonlinear control systems control design Wastewater Automation

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H∞ and QFT robust control designs for level control plant

H∞ and QFT robust control designs for level control plant

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IEEE India Conference (INDICON)

作者： Y. Kouhi B. Labibi A. Fatehi R. Adlgostar Advanced Process Automation and Control (APAC) Research Group K.N. Toosi University of Technology Tehran Iran

In this paper two robust controllers are designed for a practical process trainer level plant. The system nonlinearity, time delay and change of parameters are the main problems in design of a desired controller for this plant. To design a controller, the linear models of the system and the disturbance models at different operating points are derived. Then, a parametric uncertainty profile is obtained by system identification strategies which is used in QFT control design. Indeed, for H infin control design a multiplicative unstructured model is extracted from the parametric uncertainty. All constraints in control design, disturbance rejection and control signal are derived. Based on these constraints, appropriate controllers are determined. To improve robust performance mu-synthesis with DK iteration is used. Finally all results are compared by applying the different controllers to the plant.

关键词： Robust control Level control Uncertainty Valves control design process control control systems Pressure control Automatic control Phase frequency detector

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Nonlinear identification and MIMO control of a laboratory helicopter using genetic algorithm

Nonlinear identification and MIMO control of a laboratory he...

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IEEE India Conference (INDICON)

作者： H. Tahersima A. Fatehi Advanced Process Automation & Control (APAC) research group Faculty of Electrical Engineering Khaje Nasir Toosi University of Technology Tehran Iran

Closed loop identification of nonlinear model and control of a laboratory helicopter using genetic algorithm is proposed in this paper. The derived model has a nonlinear structure. Using the previous results of the physical modeling of the studied plant, a nonlinear model is considered based on the physical dynamics of the system. However, there is no need to perform numerous physical experiments to estimate the model parameters. Instead, genetic algorithm as a nonlinear optimization technique is used to obtain the parameters of the model. Therefore, the advantage of both modeling and identification methods are employed. In the next step, the parameters of a multi input-multi output (MIMO) PID controller for the derived model will be tuned by GA using the obtained nonlinear model as a simulator of the plant. Applying the controller to both the real plant and the simulation model, the accuracy of the model and the performance of the controller is examined. The results demonstrate that the achieved model accurately fits to the behavior of the real plant and the controller designed based on this model, can control the real system appropriately.

关键词： MIMO Laboratories Helicopters Genetic algorithms Parameter estimation Automatic control Mathematical model Electronic mail Nonlinear dynamical systems control system synthesis

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Decentralized Robust PI controller Design For An Industrial Utility Boiler - An IMC Method

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IFAC Proceedings Volumes 2008年第2期41卷 14235-14240页

作者： Horacio Jose Marquez Batool Labibi Tongwen Chen Advanced Process Automation and Control (APAC) Research Group K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Department University of Alberta Edmonton Canada

This paper presents a scheme for designing a robust decentralized PI controller for an industrial utility boiler system. First, a new method for designing robust decentralized PI controllers for uncertain LTI MIMO systems is presented. Sufficient conditions for closed-loop stability and diagonal dominance of a multivariable system are given. For each isolated subsystem a first order approximation is obtained. Then, achieving robust stability and closedloop diagonal dominance is formulated as local robust performance problems. It is shown by selecting time constants of the closed-loop isolated subsystems appropriately, these local robust performance problems are solved and the interactions between closed-loop stabilized subsystems are attenuated. The internal model control (IMC) method is used to design local PI controllers. The suggested design strategy is applicable to unstable systems as well. Thereafter, the nonlinear model of an industrial utility boiler is linearized about its operating points and the nonlinearity is modeled as uncertainty for a nominal LTI MIMO system. Using the new proposed method, a decentralized PI controller for the uncertain LTI nominal model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.

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Robust Predictive PI controller Based on First-Order Dead Time Model

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IFAC Proceedings Volumes 2008年第2期41卷 5808-5813页

作者： F. Arousi U. Schmitz R. Bars R. Haber Dept. of Automation and Applied Informatics Budapest University of Technology and Economics MTA-BME Control Research group Dept. of Advanced Process Control Shell Rheinland Refinery Köln/Wesseling Germany Inst. of Chemical Process Engineering and Plant Design University of Applied Science Cologne Germany

Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control algorithms are derived using approximation of an aperiodic process by a first-order model with dead time. Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch. The noise filter is considered as a design parameter. Simulation examples demonstrate the behavior of the predictive PI algorithm and the robustifying effect of the noise filter.

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Lipschitz Numbers: A Medium for Delay Estimation

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IFAC Proceedings Volumes 2008年第2期41卷 7468-7473页

作者： Iman Makaremi Alireza Fatehi Babak Nadjar Araabi Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control & Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran

The paper deals with problem of estimating input channel delay in nonlinear system with a model-free approach. The proposed method is based on Lipschitz theory. It is an extension to the Lipschitz method which was proposed for determining the order of a model. Our algorithm consists of two parts which in the first one estimation is made on the proper number of dynamics on the input and in the second part the pure delay of the input is obtained. The method is applied for estimation of the delay of two different models and the estimation was as accurate as possible.

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Real-Time Level Plant control Using Improved BELBIC

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IFAC Proceedings Volumes 2008年第2期41卷 4631-4635页

作者： M. Masoudinejad R. Khorsandi A. Fatehi C. Lucas S. Fakhimi Derakhshan M.R. Jamali Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Center of Excellence for Control and Intelligent Processing Electrical and Computer engineering Department University of Tehran Iran

Brain emotional learning based intelligent controller (BELBIC) is based on computational model of limbic system in the mammalian brain. In recent years, this model was applied in many linear and nonlinear control applications. Previous studies show that this controller has fast response, simple implementation and robustness with respect to disturbances. It is also possible to define emotional signal based on control application objectives. But in the previous studies, internal instability of this controller was not considered and control task were done in limited time period. In this article mathematical description of BELBIC is investigated and improved to avoid internal instability. Simulation and implementation of improved model was done on level plant. The obtained results showed that instability of model has been solved in the new model without loss of performance by using Integral Anti Windup (IAW).

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Identification and Abnormal Condition Detection of a Cement Rotary Kiln

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IFAC Proceedings Volumes 2008年第2期41卷 7233-7238页

作者： Iman Makaremi Alireza Fatehi Babak N. Araabi Morteza Azizi Ahmad Cheloian Advanced Process Automation & Control (APAC) Research Group Faculty of Electrical Eng. K.N. Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical & Computer Eng. University of Tehran Tehran Iran Saveh Cement Company Saveh Iran

In this paper, we use system identification methods for abnormal condition detection of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. A novel approach is used in order to estimate the delays of the input channel of the kiln. By means of that, the identification task gets easier and the results are more accurate. To identify the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Finally, a model for the healthy mode of the kiln is obtained through which it is possible to detect abnormal conditions in the process. We distinguished two common abnormal conditions in kiln and another one which was not characteristically known for cement experts as well.

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